{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading Platform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"-1\">\n",
    "Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits. Leverage can work against you. Due to the certain restrictions imposed by the local law and regulation, German resident retail client(s) could sustain a total loss of deposited funds but are not subject to subsequent payment obligations beyond the deposited funds. Be aware and fully understand all risks associated with the market and trading. Prior to trading any products, carefully consider your financial situation and experience level. Any opinions, news, research, analyses, prices, or other information is provided as general market commentary, and does not constitute investment advice. FXCM & TPQ will not accept liability for any loss or damage, including without limitation to, any loss of profit, which may arise directly or indirectly from use of or reliance on such information.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The author is neither an employee, agent nor representative of FXCM and is therefore acting independently. The opinions given are their own, constitute general market commentary, and do not constitute the opinion or advice of FXCM or any form of personal or investment advice. FXCM assumes no responsibility for any loss or damage, including but not limited to, any loss or gain arising out of the direct or indirect use of this or any other content. Trading forex/CFDs on margin carries a high level of risk and may not be suitable for all investors as you could sustain losses in excess of deposits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Tick Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from pylab import mpl, plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fxcmpy import fxcmpy_tick_data_reader as tdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"
     ]
    }
   ],
   "source": [
    "print(tdr.get_available_symbols())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2018, 6, 25)  \n",
    "stop = dt.datetime(2018, 6, 30)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "td = tdr('EURUSD', start, stop)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1963779 entries, 06/24/2018 21:00:12.290 to 06/29/2018 20:59:00.607\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype  \n",
      "---  ------  -----  \n",
      " 0   Bid     float64\n",
      " 1   Ask     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 44.9+ MB\n"
     ]
    }
   ],
   "source": [
    "td.get_raw_data().info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1963779 entries, 2018-06-24 21:00:12.290000 to 2018-06-29 20:59:00.607000\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype  \n",
      "---  ------  -----  \n",
      " 0   Bid     float64\n",
      " 1   Ask     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 44.9 MB\n"
     ]
    }
   ],
   "source": [
    "td.get_data().info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "                            Bid      Ask\n",
       "2018-06-24 21:00:12.290  1.1662  1.16660\n",
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       "2018-06-24 21:00:22.846  1.1662  1.16658\n",
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   "source": [
    "td.get_data().head()"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = td.get_data(start='2018-06-29 12:00:00',\n",
    "                  end='2018-06-29 12:15:00')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "                             Bid      Ask\n",
       "2018-06-29 12:00:00.011  1.16497  1.16498\n",
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    "sub.head()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['Mid'] = sub.mean(axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['SMA'] = sub['Mid'].rolling(1000).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sub[['Mid', 'SMA']].plot(figsize=(10, 6), lw=0.75);\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_01.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Candles Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fxcmpy import fxcmpy_candles_data_reader as cdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'CADCHF', 'EURAUD', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'GBPUSD', 'GBPCHF', 'GBPJPY', 'GBPNZD', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY')\n"
     ]
    }
   ],
   "source": [
    "print(cdr.get_available_symbols())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2018, 5, 1)\n",
    "stop = dt.datetime(2018, 6, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`period` must be one of `m1`, `H1` or `D1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "period = 'H1'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = cdr('EURUSD', start, stop, period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = candles.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1080 entries, 2018-04-29 21:00:00 to 2018-06-29 20:00:00\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   BidOpen   1080 non-null   float64\n",
      " 1   BidHigh   1080 non-null   float64\n",
      " 2   BidLow    1080 non-null   float64\n",
      " 3   BidClose  1080 non-null   float64\n",
      " 4   AskOpen   1080 non-null   float64\n",
      " 5   AskHigh   1080 non-null   float64\n",
      " 6   AskLow    1080 non-null   float64\n",
      " 7   AskClose  1080 non-null   float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 75.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "2018-06-29 20:00:00  1.16774  1.16904  1.16758   1.16816"
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     "execution_count": 22,
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    "data[data.columns[:4]].tail()  "
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  {
   "cell_type": "code",
   "execution_count": 23,
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   "execution_count": 26,
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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
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     },
     "metadata": {},
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   ],
   "source": [
    "data[['MidClose', 'SMA1', 'SMA2']].plot(figsize=(10, 6));\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_02.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting to the API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fxcmpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.9'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fxcmpy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "api = fxcmpy.fxcmpy(config_file='../../cfg/fxcm.cfg')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruments = api.get_instruments()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EUR/USD', 'USD/JPY', 'GBP/USD', 'USD/CHF', 'EUR/CHF', 'AUD/USD', 'USD/CAD', 'NZD/USD', 'EUR/GBP', 'EUR/JPY', 'GBP/JPY', 'CHF/JPY', 'GBP/CHF', 'EUR/AUD', 'EUR/CAD', 'AUD/CAD', 'AUD/JPY', 'CAD/JPY', 'NZD/JPY', 'GBP/CAD', 'GBP/NZD', 'GBP/AUD', 'AUD/NZD', 'USD/SEK', 'EUR/SEK', 'EUR/NOK', 'USD/NOK', 'USD/MXN', 'AUD/CHF', 'EUR/NZD', 'USD/ZAR', 'USD/HKD', 'ZAR/JPY', 'USD/TRY', 'EUR/TRY', 'NZD/CHF', 'CAD/CHF', 'NZD/CAD', 'TRY/JPY', 'USD/ILS', 'USD/CNH', 'AUS200', 'ESP35', 'FRA40', 'GER30', 'HKG33', 'JPN225', 'NAS100', 'SPX500', 'UK100', 'US30', 'Copper', 'CHN50', 'EUSTX50', 'USDOLLAR', 'US2000', 'USOil', 'UKOil', 'SOYF', 'NGAS', 'USOilSpot', 'UKOilSpot', 'WHEATF', 'CORNF', 'Bund', 'XAU/USD', 'XAG/USD', 'EMBasket', 'JPYBasket', 'BTC/USD', 'BCH/USD', 'ETH/USD', 'LTC/USD', 'XRP/USD', 'CryptoMajor', 'EOS/USD', 'XLM/USD', 'ESPORTS', 'BIOTECH', 'CANNABIS', 'FAANG', 'CHN.TECH', 'CHN.ECOMM', 'USEquities']\n"
     ]
    }
   ],
   "source": [
    "print(instruments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieving Historical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('USD/JPY', period='D1', number=10)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bidopen</th>\n",
       "      <th>bidclose</th>\n",
       "      <th>bidhigh</th>\n",
       "      <th>bidlow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-15 21:00:00</th>\n",
       "      <td>107.227</td>\n",
       "      <td>106.925</td>\n",
       "      <td>107.304</td>\n",
       "      <td>106.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-16 21:00:00</th>\n",
       "      <td>106.925</td>\n",
       "      <td>107.204</td>\n",
       "      <td>107.396</td>\n",
       "      <td>106.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-17 21:00:00</th>\n",
       "      <td>107.204</td>\n",
       "      <td>107.008</td>\n",
       "      <td>107.356</td>\n",
       "      <td>106.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20 21:00:00</th>\n",
       "      <td>107.008</td>\n",
       "      <td>107.241</td>\n",
       "      <td>107.539</td>\n",
       "      <td>107.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-21 21:00:00</th>\n",
       "      <td>107.241</td>\n",
       "      <td>106.787</td>\n",
       "      <td>107.362</td>\n",
       "      <td>106.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-22 21:00:00</th>\n",
       "      <td>106.787</td>\n",
       "      <td>107.146</td>\n",
       "      <td>107.283</td>\n",
       "      <td>106.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23 21:00:00</th>\n",
       "      <td>107.146</td>\n",
       "      <td>106.853</td>\n",
       "      <td>107.223</td>\n",
       "      <td>106.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-24 21:00:00</th>\n",
       "      <td>106.853</td>\n",
       "      <td>106.070</td>\n",
       "      <td>106.899</td>\n",
       "      <td>105.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26 21:00:00</th>\n",
       "      <td>106.078</td>\n",
       "      <td>106.002</td>\n",
       "      <td>106.127</td>\n",
       "      <td>105.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-27 21:00:00</th>\n",
       "      <td>106.002</td>\n",
       "      <td>105.265</td>\n",
       "      <td>106.099</td>\n",
       "      <td>105.259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bidopen  bidclose  bidhigh   bidlow\n",
       "date                                                    \n",
       "2020-07-15 21:00:00  107.227   106.925  107.304  106.660\n",
       "2020-07-16 21:00:00  106.925   107.204  107.396  106.827\n",
       "2020-07-17 21:00:00  107.204   107.008  107.356  106.933\n",
       "2020-07-20 21:00:00  107.008   107.241  107.539  107.007\n",
       "2020-07-21 21:00:00  107.241   106.787  107.362  106.673\n",
       "2020-07-22 21:00:00  106.787   107.146  107.283  106.705\n",
       "2020-07-23 21:00:00  107.146   106.853  107.223  106.705\n",
       "2020-07-24 21:00:00  106.853   106.070  106.899  105.676\n",
       "2020-07-26 21:00:00  106.078   106.002  106.127  105.905\n",
       "2020-07-27 21:00:00  106.002   105.265  106.099  105.259"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[candles.columns[:4]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>askopen</th>\n",
       "      <th>askclose</th>\n",
       "      <th>askhigh</th>\n",
       "      <th>asklow</th>\n",
       "      <th>tickqty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-15 21:00:00</th>\n",
       "      <td>107.243</td>\n",
       "      <td>106.945</td>\n",
       "      <td>107.317</td>\n",
       "      <td>106.672</td>\n",
       "      <td>171814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-16 21:00:00</th>\n",
       "      <td>106.945</td>\n",
       "      <td>107.284</td>\n",
       "      <td>107.407</td>\n",
       "      <td>106.839</td>\n",
       "      <td>173810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-17 21:00:00</th>\n",
       "      <td>107.284</td>\n",
       "      <td>107.059</td>\n",
       "      <td>107.379</td>\n",
       "      <td>106.945</td>\n",
       "      <td>135795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20 21:00:00</th>\n",
       "      <td>107.059</td>\n",
       "      <td>107.276</td>\n",
       "      <td>107.553</td>\n",
       "      <td>107.022</td>\n",
       "      <td>159784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-21 21:00:00</th>\n",
       "      <td>107.276</td>\n",
       "      <td>106.803</td>\n",
       "      <td>107.373</td>\n",
       "      <td>106.685</td>\n",
       "      <td>176453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-22 21:00:00</th>\n",
       "      <td>106.803</td>\n",
       "      <td>107.163</td>\n",
       "      <td>107.295</td>\n",
       "      <td>106.718</td>\n",
       "      <td>174734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-23 21:00:00</th>\n",
       "      <td>107.163</td>\n",
       "      <td>106.871</td>\n",
       "      <td>107.236</td>\n",
       "      <td>106.717</td>\n",
       "      <td>145894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-24 21:00:00</th>\n",
       "      <td>106.871</td>\n",
       "      <td>106.180</td>\n",
       "      <td>106.923</td>\n",
       "      <td>105.687</td>\n",
       "      <td>240193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26 21:00:00</th>\n",
       "      <td>106.185</td>\n",
       "      <td>106.093</td>\n",
       "      <td>106.206</td>\n",
       "      <td>105.996</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-27 21:00:00</th>\n",
       "      <td>106.093</td>\n",
       "      <td>105.275</td>\n",
       "      <td>106.132</td>\n",
       "      <td>105.270</td>\n",
       "      <td>142401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     askopen  askclose  askhigh   asklow  tickqty\n",
       "date                                                             \n",
       "2020-07-15 21:00:00  107.243   106.945  107.317  106.672   171814\n",
       "2020-07-16 21:00:00  106.945   107.284  107.407  106.839   173810\n",
       "2020-07-17 21:00:00  107.284   107.059  107.379  106.945   135795\n",
       "2020-07-20 21:00:00  107.059   107.276  107.553  107.022   159784\n",
       "2020-07-21 21:00:00  107.276   106.803  107.373  106.685   176453\n",
       "2020-07-22 21:00:00  106.803   107.163  107.295  106.718   174734\n",
       "2020-07-23 21:00:00  107.163   106.871  107.236  106.717   145894\n",
       "2020-07-24 21:00:00  106.871   106.180  106.923  105.687   240193\n",
       "2020-07-26 21:00:00  106.185   106.093  106.206  105.996      337\n",
       "2020-07-27 21:00:00  106.093   105.275  106.132  105.270   142401"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candles[candles.columns[4:]]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime(2017, 1, 1)  \n",
    "end = dt.datetime(2018, 1, 1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('EUR/GBP', period='D1',\n",
    "                          start=start, stop=end)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 309 entries, 2017-01-03 22:00:00 to 2018-01-01 22:00:00\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   bidopen   309 non-null    float64\n",
      " 1   bidclose  309 non-null    float64\n",
      " 2   bidhigh   309 non-null    float64\n",
      " 3   bidlow    309 non-null    float64\n",
      " 4   askopen   309 non-null    float64\n",
      " 5   askclose  309 non-null    float64\n",
      " 6   askhigh   309 non-null    float64\n",
      " 7   asklow    309 non-null    float64\n",
      " 8   tickqty   309 non-null    int64  \n",
      "dtypes: float64(8), int64(1)\n",
      "memory usage: 24.1 KB\n"
     ]
    }
   ],
   "source": [
    "candles.info()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter `period` must be one of `m1, m5, m15, m30, H1, H2, H3, H4, H6, H8, D1, W1` or `M1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = api.get_candles('EUR/USD', period='m1', number=250)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8529b3ef50>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "candles['askclose'].plot(figsize=(10, 6))\n",
    "# plt.savefig('../../images/ch14/fxcm_plot_03.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streaming Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output(data, dataframe):\n",
    "    print('%3d | %s | %s | %6.5f, %6.5f' \n",
    "          % (len(dataframe), data['Symbol'],\n",
    "             pd.to_datetime(int(data['Updated']), unit='ms'), \n",
    "             data['Rates'][0], data['Rates'][1]))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2 | EUR/USD | 2020-07-27 10:29:36.921000 | 1.17302, 1.17314\n",
      "  3 | EUR/USD | 2020-07-27 10:29:37.323000 | 1.17300, 1.17312\n",
      "  4 | EUR/USD | 2020-07-27 10:29:37.775000 | 1.17299, 1.17311\n",
      "  5 | EUR/USD | 2020-07-27 10:29:38.799000 | 1.17301, 1.17314\n",
      "  6 | EUR/USD | 2020-07-27 10:29:39.333000 | 1.17299, 1.17311\n",
      "  7 | EUR/USD | 2020-07-27 10:29:39.649000 | 1.17300, 1.17313\n",
      "  8 | EUR/USD | 2020-07-27 10:29:40.158000 | 1.17301, 1.17313\n"
     ]
    }
   ],
   "source": [
    "api.subscribe_market_data('EUR/USD', (output,))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  9 | EUR/USD | 2020-07-27 10:29:40.479000 | 1.17300, 1.17313\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Bid     1.17300\n",
       "Ask     1.17313\n",
       "High    1.17339\n",
       "Low     1.16409\n",
       "Name: 2020-07-27 10:29:40.479000, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_last_price('EUR/USD')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.unsubscribe_market_data('EUR/USD')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Placing Orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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     "execution_count": 44,
     "metadata": {},
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   ],
   "source": [
    "api.get_open_positions()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/USD', 100)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = ['tradeId', 'amountK', 'currency',\n",
    "       'grossPL', 'isBuy']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -11.08222   True"
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     "execution_count": 47,
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    "api.get_open_positions()[sel]  "
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/GBP', 50)  "
   ]
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -19.60868   True\n",
       "1  168818204       50  EUR/GBP -12.06709   True"
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     "execution_count": 49,
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    "api.get_open_positions()[sel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_sell_order('EUR/USD', 25)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = api.create_market_buy_order('EUR/GBP', 50)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -11.93480   True\n",
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       "3  168818208       50  EUR/GBP -19.74810   True"
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     "execution_count": 52,
     "metadata": {},
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   "source": [
    "api.get_open_positions()[sel]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.close_all_for_symbol('EUR/GBP')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "     tradeId  amountK currency   grossPL  isBuy\n",
       "0  168818203      100  EUR/USD -14.49263   True\n",
       "1  168818206       25  EUR/USD  -4.04896  False"
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   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "api.close_all()  "
   ]
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   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "api.get_open_positions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Account Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1233279"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_default_account()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>t</th>\n",
       "      <td>6</td>\n",
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       "      <th>ratePrecision</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accountId</th>\n",
       "      <td>1233279</td>\n",
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       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>49665.4</td>\n",
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       "    <tr>\n",
       "      <th>usdMr</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>mc</th>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcDate</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accountName</th>\n",
       "      <td>01233279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usdMr3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hedging</th>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin3</th>\n",
       "      <td>49665.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMarginPerc</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin3Perc</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>equity</th>\n",
       "      <td>49665.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usableMargin</th>\n",
       "      <td>49665.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayPL</th>\n",
       "      <td>-85.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grossPL</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0\n",
       "t                         6\n",
       "ratePrecision             0\n",
       "accountId           1233279\n",
       "balance             49665.4\n",
       "usdMr                     0\n",
       "mc                        N\n",
       "mcDate                     \n",
       "accountName        01233279\n",
       "usdMr3                    0\n",
       "hedging                   Y\n",
       "usableMargin3       49665.4\n",
       "usableMarginPerc        100\n",
       "usableMargin3Perc       100\n",
       "equity              49665.4\n",
       "usableMargin        49665.4\n",
       "bus                    1000\n",
       "dayPL                -85.34\n",
       "grossPL                   0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api.get_accounts().T  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
   ]
  }
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